How to Combine DataFrames with Different Indexes: Append Method Explained

Combining Two DataFrames with Different Indexes

When working with dataframes, you may encounter situations where you need to combine two dataframes extracted from a larger dataset. Suppose you have an initial dataframe D and extract two dataframes A and B from it as follows:

<code class="python">A = D[D.label == k]
B = D[D.label != k]</code>

Your goal is to combine A and B into a single dataframe without regard to their order. However, these dataframes retain their indexes from the original D dataset.

To address this, you can utilize the append method. The syntax is as follows:

<code class="python">df_merged = df1.append(df2, ignore_index=True)</code>

Setting ignore_index to True ensures that the resulting dataframe df_merged has a new sequence of indexes instead of concatenating the indexes of df1 and df2.

If you prefer to preserve the original indexes of A and B, you can set ignore_index to False:

<code class="python">df_merged = df1.append(df2, ignore_index=False)</code>

By using append, you can conveniently combine dataframes while handling index management as needed.

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